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Bayesian Tensor Decomposition for Signal Processing and Machine Learning

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Cover of 'Bayesian Tensor Decomposition for Signal Processing and Machine Learning'

Table of Contents

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    Book Overview
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    Chapter 1 Tensor Decomposition: Basics, Algorithms, and Recent Advances
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    Chapter 2 Bayesian Learning for Sparsity-Aware Modeling
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    Chapter 3 Bayesian Tensor CPD: Modeling and Inference
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    Chapter 5 When Stochastic Optimization Meets VI: Scaling Bayesian CPD to Massive Data
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    Chapter 6 Bayesian Tensor CPD with Nonnegative Factors
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    Chapter 7 Complex-Valued CPD, Orthogonality Constraint, and Beyond Gaussian Noises
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    Chapter 8 Handling Missing Value: A Case Study in Direction-of-Arrival Estimation
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    Chapter 9 From CPD to Other Tensor Decompositions
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